Highlights d Robust method automatically adapting to various unseen experimental scenarios d Deep learning solution for accurate nucleus segmentation without user interaction d Accelerates, improves quality, and reduces complexity of bioimage analysis tasks

Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require a massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of theimage for prediction and the results are merged. in this paper we describe how we have incorporated the test-time argumentation prediction method into two major segmentation approaches utilized in the single-cell analysis of microscopy images. these approaches are semantic segmentation based on the U-Net, and instance segmentation based on the Mask R-CNN models. Our findings show that even if only simple test-time augmentations (such as rotation or flipping and proper merging methods) are applied, TTA can significantly improve prediction accuracy. We have utilized images of tissue and cell cultures from the Data Science Bowl (DSB) 2018 nuclei segmentation competition and other sources. Additionally, boosting the highest-scoring method of the DSB with TTA, we could further improve prediction accuracy, and our method has reached an ever-best score at the DSB.

Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is to adapt our model to unseen and unlabeled data using image style transfer to generate augmented training samples. This allows the model to recognize nuclei in new and different experiments without requiring expert annotations.Identifying nuclei is the starting point for many microscopy-based cellular analyses. Accurate localization of the nucleus is the basis of a variety of quantitative measurements, but is also a first step for identifying individual cell borders, which enables a multitude of further analyses. Until recently, the dominant approaches for this task have been based on classic image processing algorithms (e.g. CellProfiler 1 ) which were sometimes guided by shape and spatial priors 2 . A drawback of these methods is the need for expert knowledge to properly adjust the parameters, which typically must be re-tuned when experimental conditions change.Recently, deep learning has revolutionized an assortment of tasks in image analysis, from image classification 3 to face recognition 4 , and scene segmentation 5 . It is also responsible for breakthroughs in diagnosing retinal images 6 , classifying skin lesions with superhuman performance 7 , as well as incredible advances in 3D fluorescence image analysis 8 . However, aside from initial works from Caicedo et al. 9 and Van Valen et al. 10 , deep learning has yet to significantly advance nucleus segmentation performance.